PT  - JOURNAL ARTICLE
AU  - Kimberly A Dill-McFarland
AU  - Stephan G König
AU  - Florent Mazel
AU  - David Oliver
AU  - Lisa M McEwen
AU  - Kris Y Hong
AU  - Steven J Hallam
TI  - An integrated, modular approach to data science education in the life sciences
AID  - 10.1101/2020.07.25.218453
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.25.218453
4099  - http://biorxiv.org/content/early/2020/07/27/2020.07.25.218453.short
4100  - http://biorxiv.org/content/early/2020/07/27/2020.07.25.218453.full
AB  - We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, post-secondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has potential to improve research and learning outcomes and better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated co-curricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants, postdocs, instructors and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.Availability and implementation The EDUCE teaching and learning framework is accessible at educe-ubc.github.ioCompeting Interest StatementSJH is a co-founder of Koonkie Inc., a bioinformatics consulting company that designs and provides scalable algorithmic and data analytics solutions in the cloud